we use a dat set obatained from the UC Irvine Machine Learning Repository which contains information related to a direct marketing compaign of a Portuguese banking institution and its attempts to get its clients to subscribe for a team deposit.

The dataset includes a total of 41,188 records, with each record containing 20 attributes. These attributes provide information about the client's demographic profile, their financial history, and their previous interactions with the bank.

The target variable in this dataset is whether the client subscribed to a term deposit or not, with a value of either "yes" or "no". The dataset is highly imbalanced, with only 11.27% of the records corresponding to clients who subscribed to a term deposit.

The dataset can be used to build a predictive model that can help the bank identify which clients are most likely to subscribe to a term deposit, and thereby optimize their marketing efforts. The model can also help the bank understand the factors that drive clients to subscribe to a term deposit, which can in turn inform the bank's product development and marketing strategies.